# 导入模块
from selenium import webdriver
from selenium.webdriver import ActionChains
from PIL import Image
import time

def get_snap(driver):
    driver.save_screenshot('full_snap.png')
    page_snap_obj = Image.open('full_snap.png')
    return page_snap_obj

def get_image(driver):
    img = driver.find_element_by_class_name('geetest_canvas_img')
    time.sleep(2)
    location = img.location
    size = img.size
    print(size)

    left = location['x']
    top = location['y']
    right = left+size['width']
    bottom = top+size['height']

    page_snap_obj = get_snap(driver)
    image_obj = page_snap_obj.crop((left, top, right, bottom))
    # image_obj.show()
    return image_obj

def get_distance(image1, image2):
    start = 57
    threhold = 60

    for i in range(start, image1.size[0]):
        for j in range(image1.size[1]):
            rgb1 = image1.load()[i, j]
            rgb2 = image2.load()[i, j]
            res1 = abs(rgb1[0]-rgb2[0])
            res2 = abs(rgb1[1]-rgb2[1])
            res3 = abs(rgb1[2]-rgb2[2])
            # print(res1,res2,res3)
            if not (res1 <threhold and res2 <threhold and res3 < threhold):
                return i - 7
    return i - 7

def get_tracks(distance):
    distance += 20  # 先滑过一点, 最后再反着滑动回来
    v = 0
    t = 0.2
    forward_tracks = []

    current = 0
    mid = distance * 3 / 5
    while current < distance:
        if current < mid:
            a = 2
        else:
            a = -3

        s = v * t + 0.5 * a * (t**2)
        v = v + a * t
        current += s
        forward_tracks.append(round(s))

    # 反着滑动到准确位置
    back_tracks = [-1, -1, -1, -2, -3, -2, -2, -2, -2, -1, -1, -1]  # 总共等于 -20

    return {'forward_tracks':forward_tracks, 'back_tracks':back_tracks}

def crack(driver):  # 破解滑动认证
    # 1.点击按钮,得到没有缺口的图片
    button = driver.find_element_by_class_name('geetest_radar_tip')
    button.click()

    # 2.获取没有缺口的图片
    image1 = get_image(driver)

    # 3.点击滑动按钮,得到有缺口的图片
    button = driver.find_element_by_class_name('geetest_slider_button')
    button.click()

    # 4.获取有缺口的图片
    image2 = get_image(driver)

    # 5.对比两种图片的像素点,找出位移
    distance = get_distance(image1, image2)

    # 6.模拟人的行为习惯,根据总位移得到的行为轨迹
    tracks = get_tracks(distance)
    print(tracks)

    # 7.按照人行动轨迹先正向滑动,后反向滑动
    button = driver.find_element_by_class_name('geetest_slider_button')
    ActionChains(driver).click_and_hold(button).perform()

    # 正常人类总是自信满满地开始正向滑动,自信的表现是疯狂加速
    for track in tracks['forward_tracks']:
        ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()

    # 结果傻逼了,正常的人类停顿了一下,回过神来发现,卧槽,滑过了,然后开始反向滑动
    time.sleep(0.3)
    for back_track in tracks['back_tracks']:
        ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform()

    # 小范围震荡一下, 进一步迷惑极验后台, 这一步可以极大的提高成功率
    time.sleep(0.1)
    ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()
    ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()

    time.sleep(0.5)
    ActionChains(driver).release().perform()

def login_cnblogs(username,password):
    driver = webdriver.Chrome()
    try:
        # 1、输入账号密码回车
        driver.implicitly_wait(3)
        driver.get('https://passport.cnblogs.com/user/signin')

        input_username = driver.find_element_by_id('input1')
        input_pwd = driver.find_element_by_id('input2')
        signin = driver.find_element_by_id('signin')

        input_username.send_keys(username)
        input_pwd.send_keys(password)
        signin.click()

        time.sleep(10)
        # 2、破解滑动认证
        crack(driver)
        time.sleep(10)  # 睡时间长一点，确定登录成功
        # with open('cnlog.html','w',encoding='utf-8') as page:
        #     text = driver.page_source
        #     page.write(text)
        # pass
    finally:
        driver.close()



if __name__ == '__main__':
    login_cnblogs(username='aaaaa',password='aaaaa')